This PPG integrates multiple disciplines to apply X-ray structural studies, presteady state kinetic and theoretical computational analyses and novel chemical probes to elucidate the molecular basis of DNA polymerase catalysis incorporating base-pair discrimination, a fundamental issue in mutagenesis relevant to cancer. The Program Project contains three research projects, structural (Project 1), theoretical computational (Project 2), and kinetics coupled with an approach toward translational paths (Project 3). Our success at synthesizing dNTP substrate analogs, by replacing one or both phosphate bridging oxygen molecules with a large variety of halo-methylene derivatives containing widely differing electrostatic charge and steric properties, allows us to probe fidelity from a transitions state (T) perspective. The use of these substrate analogs is a uniquely powerful aspect of our PPG, and will allow us for the first time to investigate TS effects using stereoisomeric probes, while offering a feasible approach for targeted inhibition of Pol p, on a path toward cancer cell inhibition (Project 3). The objective of Project 1 is to obtain high-resolution structural data for normal and aberrant forms of Pol , using the dNTP analogs designed in Project 3 and synthesized in Core B. The goal of Project 2 is the application of theoretical and computer modeling to perform structure/function analyses of catalytic mechanisms that govern base selection both in the ground-state and TS. The computations are aimed at calculating free energies, which are used to predict individual contributions of amino acid side chains to fidelity, including substrate binding and catalysis in the pol active site. Central to our PPG is that the theory (Project 2) serves as the intellectual framework with which to marry structural analysis (Project 1) with kinetic mechanistic analysis (Project 3). It is atypical for the experimentalist t test a priori computational predictions. A defining aspect of this PPG is its bidirectional interply, where structural data serve as a starting point for computational predictions, which are tested experimentally, and where the experimental data are used to refine the theory.